Azure Monitor and Graphite Integration
Powerful performance with an easy integration, powered by Telegraf, the open source data connector built by InfluxData.
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Table of Contents
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
Input and output integration overview
Gather metrics from Azure resources using the Azure Monitor API.
The Graphite plugin enables users to send metrics collected by Telegraf into Graphite via TCP. This integration allows for efficient storage and visualization of time-series data using Graphite’s powerful capabilities.
Integration details
Azure Monitor
The Azure Monitor Telegraf plugin is specifically designed for gathering metrics from various Azure resources using the Azure Monitor API. Users must provide specific credentials such as client_id
, client_secret
, tenant_id
, and subscription_id
to authenticate and gain access to their Azure resources. Additionally, the plugin supports functionality to collect metrics from both individual resources and resource groups or subscriptions, allowing for flexible and scalable metric collection tailored to user needs. This plugin is ideal for organizations leveraging Azure cloud infrastructure, providing crucial insights into resource performance and utilization over time, facilitating proactive management and optimization of cloud resources.
Graphite
This plugin writes metrics to Graphite via raw TCP, allowing for seamless integration of Telegraf collected metrics into the Graphite ecosystem. With this plugin, users can configure multiple TCP endpoints for load balancing, ensuring high availability and reliability in metric transmission. The ability to customize metric naming with prefixes and utilize various templating options enhances flexibility in how data is represented within Graphite. Additionally, support for Graphite tags and options for strict sanitization of metric names allow for robust data management, catering to the varying needs of users. This capability is essential for organizations looking to leverage Graphite’s powerful metrics storage and visualization while maintaining control over data representation.
Configuration
Azure Monitor
# Gather Azure resources metrics from Azure Monitor API
[[inputs.azure_monitor]]
# can be found under Overview->Essentials in the Azure portal for your application/service
subscription_id = "<>"
# can be obtained by registering an application under Azure Active Directory
client_id = "<>"
# can be obtained by registering an application under Azure Active Directory.
# If not specified Default Azure Credentials chain will be attempted:
# - Environment credentials (AZURE_*)
# - Workload Identity in Kubernetes cluster
# - Managed Identity
# - Azure CLI auth
# - Developer Azure CLI auth
client_secret = "<>"
# can be found under Azure Active Directory->Properties
tenant_id = "<>"
# Define the optional Azure cloud option e.g. AzureChina, AzureGovernment or AzurePublic. The default is AzurePublic.
# cloud_option = "AzurePublic"
# resource target #1 to collect metrics from
[[inputs.azure_monitor.resource_target]]
# can be found under Overview->Essentials->JSON View in the Azure portal for your application/service
# must start with 'resourceGroups/...' ('/subscriptions/xxxxxxxx-xxxx-xxxx-xxx-xxxxxxxxxxxx'
# must be removed from the beginning of Resource ID property value)
resource_id = "<>"
# the metric names to collect
# leave the array empty to use all metrics available to this resource
metrics = [ "<>", "<>" ]
# metrics aggregation type value to collect
# can be 'Total', 'Count', 'Average', 'Minimum', 'Maximum'
# leave the array empty to collect all aggregation types values for each metric
aggregations = [ "<>", "<>" ]
# resource target #2 to collect metrics from
[[inputs.azure_monitor.resource_target]]
resource_id = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# resource group target #1 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.resource_group_target]]
# the resource group name
resource_group = "<>"
# defines the resources to collect metrics from
[[inputs.azure_monitor.resource_group_target.resource]]
# the resource type
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# defines the resources to collect metrics from
[[inputs.azure_monitor.resource_group_target.resource]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# resource group target #2 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.resource_group_target]]
resource_group = "<>"
[[inputs.azure_monitor.resource_group_target.resource]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# subscription target #1 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.subscription_target]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
# subscription target #2 to collect metrics from resources under it with resource type
[[inputs.azure_monitor.subscription_target]]
resource_type = "<>"
metrics = [ "<>", "<>" ]
aggregations = [ "<>", "<>" ]
</code></pre>
Graphite
# Configuration for Graphite server to send metrics to
[[outputs.graphite]]
## TCP endpoint for your graphite instance.
## If multiple endpoints are configured, the output will be load balanced.
## Only one of the endpoints will be written to with each iteration.
servers = ["localhost:2003"]
## Local address to bind when connecting to the server
## If empty or not set, the local address is automatically chosen.
# local_address = ""
## Prefix metrics name
prefix = ""
## Graphite output template
## see https://github.com/influxdata/telegraf/blob/master/docs/DATA_FORMATS_OUTPUT.md
template = "host.tags.measurement.field"
## Strict sanitization regex
## This is the default sanitization regex that is used on data passed to the
## graphite serializer. Users can add additional characters here if required.
## Be aware that the characters, '/' '@' '*' are always replaced with '_',
## '..' is replaced with '.', and '\' is removed even if added to the
## following regex.
# graphite_strict_sanitize_regex = '[^a-zA-Z0-9-:._=\p{L}]'
## Enable Graphite tags support
# graphite_tag_support = false
## Applied sanitization mode when graphite tag support is enabled.
## * strict - uses the regex specified above
## * compatible - allows for greater number of characters
# graphite_tag_sanitize_mode = "strict"
## Character for separating metric name and field for Graphite tags
# graphite_separator = "."
## Graphite templates patterns
## 1. Template for cpu
## 2. Template for disk*
## 3. Default template
# templates = [
# "cpu tags.measurement.host.field",
# "disk* measurement.field",
# "host.measurement.tags.field"
#]
## timeout in seconds for the write connection to graphite
# timeout = "2s"
## Optional TLS Config
# tls_ca = "/etc/telegraf/ca.pem"
# tls_cert = "/etc/telegraf/cert.pem"
# tls_key = "/etc/telegraf/key.pem"
## Use TLS but skip chain & host verification
# insecure_skip_verify = false
Input and output integration examples
Azure Monitor
-
Dynamic Resource Monitoring: Use the Azure Monitor plugin to dynamically gather metrics from Azure resources based on specific criteria like tags or resource types. Organizations can automate the process of loading and unloading resource metrics, enabling better performance tracking and optimization based on resource utilization patterns.
-
Multi-Cloud Monitoring Integration: Integrate metrics collected from Azure Monitor with other cloud providers using a centralized monitoring solution. This allows organizations to view and analyze performance data across multiple cloud deployments, providing a holistic overview of resource performance and costs, and streamlining operations.
-
Anomaly Detection and Alerting: Leverage the metrics gathered via the Azure Monitor plugin in conjunction with machine learning algorithms to detect anomalies in resource utilization. By establishing baseline performance metrics and automatically alerting on deviations, organizations can mitigate risks and address performance issues before they escalate.
-
Historical Performance Analysis: Use the collected Azure metrics to conduct historical analysis by feeding the data into a data warehousing solution. This enables organizations to track trends over time, allowing for detailed reporting and decision-making based on historical performance data.
Graphite
-
Dynamic Metric Visualization: The Graphite plugin can be utilized to feed real-time metrics from various sources, such as application performance data or server health metrics, into Graphite. This dynamic integration allows teams to create interactive dashboards that visualize key performance indicators, track trends over time, and make data-driven decisions to enhance system performance.
-
Load Balanced Metrics Collection: By configuring multiple TCP endpoints within the plugin, organizations can implement load balancing for metric transmission. This use case ensures that metric delivery is both resilient and efficient, reducing the risk of data loss during high-traffic periods and maintaining a reliable flow of information to Graphite.
-
Customized Metrics Tagging: With support for Graphite tags, users can employ the Graphite plugin to enhance the granularity of their metrics. Tagging metrics with relevant information, such as application environment or service type, allows for more refined queries and analytics, enabling teams to drill down into specific areas of interest for better operational insights.
-
Enhanced Data Sanitization: Leveraging the plugin’s strict sanitization options, users can ensure that their metric names comply with Graphite’s requirements. This proactive measure eliminates potential issues arising from invalid characters in metric names, allowing for cleaner data management and more accurate visualizations.
Feedback
Thank you for being part of our community! If you have any general feedback or found any bugs on these pages, we welcome and encourage your input. Please submit your feedback in the InfluxDB community Slack.
Powerful Performance, Limitless Scale
Collect, organize, and act on massive volumes of high-velocity data. Any data is more valuable when you think of it as time series data. with InfluxDB, the #1 time series platform built to scale with Telegraf.
See Ways to Get Started
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